ArticlePediatric transplantation2019
Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data.
Article in Pediatric transplantation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Health advocates to address social adversity in pediatric liver transplantation: A clinical trial.Hepatology communications · 2026Trial
- Material economic hardships are associated with second-year hospitalizations after pediatric liver transplantation: Results from the SOCIAL-Tx study.Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2026Article
- Defining the Society of Pediatric Liver Transplantation (SPLIT) research agenda: A Delphi consensus project.Hepatology communications · 2026Article
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Coproducing a health advocate intervention for pediatric liver transplant recipients using a human-centered design.Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2025Article
- Sarcopenia and frailty: An in-depth analysis of the pathophysiology and effect on liver transplant candidates.World journal of hepatology · 2025Review
- Predicting Organ Rejections for Pediatric Heart Transplantations with a Combined Use of Transplant Registry Data and Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Machine learning in solid organ transplantation: Charting the evolving landscape.World journal of transplantation · 2025Article
- Artificial Intelligence in Pediatric Liver Transplantation: Opportunities and Challenges of a New Era.Children (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches.JMIR cardio · 2023Article
- Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering.Medicina (Kaunas, Lithuania) · 2023Article
- Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.Medicines (Basel, Switzerland) · 2023Article
- Distinct phenotypes of kidney transplant recipients aged 80 years or older in the USA by machine learning consensus clustering.BMJ surgery, interventions, & health technologies · 2023Article
- Revisiting transplant immunology through the lens of single-cell technologies.Seminars in immunopathology · 2023Review
- Distinct clinical profiles and post-transplant outcomes among kidney transplant recipients with lower education levels: uncovering patterns through machine learning clustering.Renal failure · 2023Article
- Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States.Journal of clinical medicine · 2022Article
- Distinct Phenotypes of Kidney Transplant Recipients in the United States with Limited Functional Status as Identified through Machine Learning Consensus Clustering.Journal of personalized medicine · 2022Article
- Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes.Transplant international : official journal of the European Society for Organ Transplantation · 2022Article
- Racial/ethnic disparities in wait-list outcomes are only partly explained by socioeconomic deprivation among children awaiting liver transplantation.Hepatology (Baltimore, Md.) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Machine learning analyses allow for the consideration of numerous variables in order to accommodate complex relationships that would not otherwise be apparent in traditional statistical methods to better classify patient risk. The SPLIT registry data were analyzed to determine whether baseline demographic factors and clinical/biochemical factors in the first-year post-transplant could predict ideal outcome at 3 years (IO-3) after LT. Participants who received their first, isolated LT between 2002 and 2006 and had follow-up data 3 years post-LT were included. IO-3 was defined as alive at 3 years, normal ALT (<50) or GGT (<50), normal GFR, no non-liver transplants, no cytopenias, and no PTLD. Heat map analysis and RFA were used to characterize the impact of baseline and 1-year factors on IO-3. 887/1482 SPLIT participants met inclusion criteria; 334 had IO-3. Demographic, biochemical, and clinical variables did not elucidate a visual signal on heat map analysis. RFA identified non-white race (vs white race), increased length of operation, vascular and biliary complications within 30 days, and duct-to-duct biliary anastomosis to be negatively associated with IO-3. UNOS regions 2 and 5 were also identified as important factors. RFA had an accuracy rate of 0.71 (95% CI: 0.68-0.74), PPV = 0.83, and NPV = 0.70. RFA identified participant variables that predicted IO-3. These findings may allow for better risk stratification and personalization of care following pediatric liver transplantation.
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